In this section we demonstrate the results of our domain adaptation framework and compare it against four domain adaptation methods; Direct transfer (DT): Inference of the source trained model on target data, Current SOTA: ST3D [1]} by Yang and SFUDA3D [2]} by Saltori for their corresponding base networks, Statistical normalization (SN) [3]}: weakly supervised approach that uses the target domain bounding-box statistics, Pseudo-label self training (ST): Re-training the object detector on thresholded source-model generated pseudo-labels. For reference we, also compare with the “oracle" results, which are obtained by training the detector with the ground truth labels of the target domain, indicating the possible upper bound of performance after adaptation.
<FIGURE><TABLE>